A highly distributed ecosystem may be a complex information system having a logically defined mesh of computing resources. The computing resources may be controlled by logical constructs that obtain, disseminate, process, and prepare information in useful forms for both machine as well as human consumption. The distributed ecosystems that are currently available typically utilize a centralized, monolithic approach to perform analysis and decision making tasks. That is, in other words, the distributed ecosystems that are currently available usually include a centralized process point or computing resource that performs analysis and decision making tasks for the entire distributed ecosystem.
The current approach for performing analysis and decision making tasks in a distributed ecosystem could be improved in order to better meet the dynamic nature of distributed analytics. For example, data needs to be transmitted to the centralized computing resource of the highly distributed ecosystem first before any analysis or decision making may be performed with the current approach. Therefore, there is time involved in transporting data from the location of origin to the analysis location (i.e., the centralized computing resource) and the business continuity challenge of sustaining centralized computing resources. In some instances, it may actually be less time consuming for the data to be analyzed in another location than the centralized computing resource. Thus, there exists a continuing need in the art for an improved approach for analyzing data and performing decision making tasks in a highly distributed ecosystem.